Tunnel lining segment intelligent monitoring and quantitative evaluation method for tunnel apparent inspection robot

By employing technologies such as multi-source point cloud data fusion and adaptive filtering fitting, intelligent health status assessment of tunnel lining has been achieved, solving the problems of low efficiency, unstable accuracy, and insufficient intelligence in traditional tunnel monitoring methods, and improving the assessment accuracy and comprehensiveness of tunnel lining.

CN122115946APending Publication Date: 2026-05-29SOUTHWEST JIAOTONG UNIV +1
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
SOUTHWEST JIAOTONG UNIV
Filing Date
2026-02-03
Publication Date
2026-05-29

AI Technical Summary

Technical Problem

Traditional tunnel monitoring methods are inefficient, have unstable accuracy, are difficult to achieve comprehensive coverage, have high noise in point cloud data, use a single fitting model, lack multi-dimensional coupling analysis, and have insufficient intelligent disease identification and early warning mechanisms.

Method used

A method combining multi-source point cloud data fusion, adaptive filtering and fitting, intelligent feature extraction, multi-dimensional deformation index calculation and disease coupling assessment is adopted to conduct intelligent monitoring and quantitative assessment of tunnel lining segments using a tunnel appearance inspection robot.

Benefits of technology

It achieves high-precision, high-efficiency, and intelligent health status assessment of tunnel lining, improves data integrity and the comprehensiveness of assessment, adapts to different tunnel types and working conditions, and has the characteristics of high system integration and strong scalability.

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Abstract

The application discloses a kind of tunnel apparent inspection robot lining section intelligent monitoring and quantification evaluation method, it is related to tunnel engineering structure health monitoring and intelligent evaluation technical field, comprising: tunnel apparent inspection robot is to the historical tunnel point cloud data and current tunnel point cloud data of tunnel lining collected, point cloud data fusion is carried out in turn, tunnel section and unit division, point cloud filtering denoising, multi-model adaptive fitting, feature point extraction, curve fitting processing, obtain historical best projection point coordinate and current best projection point coordinate;Using historical best projection point coordinate and current best projection point coordinate, carry out multi-dimensional deformation index calculation, and based on multi-dimensional deformation index calculation result carries out disease intelligent identification and coupling evaluation.The application is through multi-source point cloud data fusion, adaptive filtering and fitting, intelligent feature extraction, multi-dimensional deformation index calculation and disease coupling evaluation, realize the overall, accurate, intelligent health state evaluation of tunnel lining.
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